Mobile communication system using subcoding techniques

ABSTRACT

The disclosure relates to a mobile communication system including: a first transmission path configured to transmit a message according to a first radio access technology; a second transmission path configured to transmit the message according to a second radio access technology; and an encoder configured to encode the message by a code before transmission of the message over the first transmission path and the second transmission path, wherein the code comprises at least two subcodes, and wherein the encoder is configured to encode the message intended for transmission over the first transmission path with a first subcode of the at least two subcodes and to encode the message intended for transmission over the second transmission path with a second subcode of the at least two subcodes.

FIELD

The disclosure relates to a mobile communication system using subcodingtechniques, a mobile receiver using subcoding techniques, a method forgenerating a super LDPC (Low Density Parity Check) code and a method forgenerating a Multi-Edge LDPC code. In particular, the disclosure relatesto techniques for joint channel coding for heterogeneous radioenvironments with respect to 5G networks, in particular to Multi-EdgeLDPC coding for inter-cell-interference management, Multi-Edge LDPCcoding sub-code selection and combination for joint multi-communicationcoding and optimization of degree distribution for Multi-Edge LDPCcoding.

BACKGROUND

In mobile communication systems such as LTE (or beyond), interferencefrom neighboring radio cells onto a serving radio cell typically reducesthe SINR (Signal to Interference plus Noise Ratio) on those ResourceBlocks of the serving cell(s) on which the neighboring cell(s)transmit(s) training sequences or data symbols. Known solutions use achannel code for coding the resource blocks in order to reduce theinterference. Optimizations can be performed through suitable choice ofconstellations (e.g., BPSK, QPSK, QAM-16, QAM-64, etc.) or through amodification of the code rate through puncturing (e.g., R=½, ¾, etc.).As mobile communication systems steadily have to be improved there is aneed to further reduce the interference from neighboring cells onto theserving cell.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of embodiments and are incorporated in and constitute apart of this specification. The drawings illustrate embodiments andtogether with the description serve to explain principles ofembodiments. Other embodiments and many of the intended advantages ofembodiments will be readily appreciated as they become better understoodby reference to the following detailed description.

FIG. 1a is a system diagram illustrating a mobile communication system100 a including different Radio Access Technologies (RATs) 11, 12 forcommunication between a base station 10 and a mobile station 20 and FIG.1b is a block diagram illustrating a mobile communication system 100 busing joint encoding across multiple Radio Access Technologies (RATs).

FIG. 2 is a schematic diagram illustrating Multi-Edge LDPC (Low DensityParity Check) coding 200.

FIG. 3 is a graph 300 illustrating an exemplary probability densityfunction over channel gains.

FIG. 4 is a graph 400 illustrating an exemplary quantized representationof channel gains.

FIG. 5a is a schematic diagram illustrating an exemplary LDPC code 500 awith various Edge Types (in this Example with Edge Types G, B and R).

FIG. 5b is a schematic diagram illustrating an exemplary LDPC code 500 bwith various Edge Types (in this Example with Edge Types G and R).

FIG. 6 is a schematic diagram illustrating an exemplary Multi-Edge LDPCsub-Code mapping 600 onto Resource Blocks not undergoing interferencefrom neighboring cells.

FIG. 7 is a schematic diagram illustrating an exemplary Multi-Edge LDPCsub-Code mapping 700 onto Resource Blocks undergoing interference fromneighboring cells.

FIG. 8a is a schematic diagram illustrating allocation of resourceblocks 800 a for a MIMO Reference Signal scenario.

FIG. 8b is a schematic diagram illustrating allocation of resourceblocks 800 b for a MIMO Reference Signal scenario with Channel StateInformation (CSI) Reference Signals.

FIG. 9 is a block diagram illustrating a mobile receiver 900 detectingmultiple interference scenarios by decoding with multiple subcodes.

FIG. 10 schematically illustrates an exemplary method 1000 forgenerating a super LDPC code.

FIG. 11 schematically illustrates an exemplary method 1100 forgenerating a Multi-Edge LDPC code.

FIG. 12 is a graph illustrating an exemplary extrinsic informationexchange (EXIT) chart 1200 for a degree distribution of a first edgetype.

FIG. 13 is a graph illustrating an exemplary extrinsic informationexchange (EXIT) chart 1300 for a degree distribution of a second edgetype.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part thereof, and in which is shownby way of illustration specific aspects in which the invention may bepracticed. It is understood that other aspects may be utilized andstructural or logical changes may be made without departing from thescope of the present invention. The following detailed description,therefore, is not to be taken in a limiting sense, and the scope of thepresent invention is defined by the appended claims.

The following terms, abbreviations and notations will be used herein:

-   3GPP: 3rd Generation Partnership Project,-   LTE: Long Term Evolution,-   LTE-A: LTE Advanced, Release 10 and higher versions of 3GPP LTE,-   RF: Radio Frequency,-   UE: User Equipment,-   eNodeB,-   eNB: base station,-   MIMO: Multiple Input Multiple Output,-   AP: Antenna Port,-   LDPC: Low Density Parity Check,-   DMRS: demodulation specific reference signal,-   RE: resource element,-   ABS: almost blank subframes,-   (e)ICIC (enhanced) inter cell interference cancellation,-   EXIT: extrinsic information transfer,-   EMI: extrinsic mutual information,-   CN: check node,-   VN: variable node.

The methods, systems and devices described herein may apply LDPC coding.It is understood that comments made in connection with a describedmethod may also hold true for a corresponding device configured toperform the method and vice versa. For example, if a specific methodstep is described, a corresponding device may include a unit to performthe described method step, even if such a unit is not explicitlydescribed or illustrated in the figures. Further, it is understood thatthe features of the various exemplary aspects described herein may becombined with each other, unless specifically noted otherwise.

The methods, systems and devices described herein may be based on LDPCcoding or any other code which can be represented in a graph such as theTanner Graph. In iterative decoding, multiple decoders make estimationson c and exchange these estimations iteratively. LDPC codes are wellsuited for iterative decoding. The computational complexity of iterativedecoding of LDPC codes grows linearly in n due to the sparsity of H. Thetwo components of LDPC decoders are the variable nodes with theirrepetition code constraint and the check nodes with their single-paritycheck constraint. The parity check matrix can be represented as abipartite graph called Tanner graph. A Tanner graph consists of a set ofvariable nodes V1; V2; : : : ; Vn, a set of check nodes C1; C2; : : : ;Cm and a set of edges E. Each column of H, i.e. codeword bit, isassociated with a variable node. Each row of H, i.e. parity checkequation, corresponds to a check node. The Tanner graph is useful toillustrate the iterative decoding process for LDPC codes. Variable nodesand check nodes estimate û_(i) and send these estimations as messagesalong the edges of the Tanner graph. The iterative decoding performanceis directly linked to the distribution of its node degrees. All codewordbit estimations are soft quantized and the variable node and check nodecomponent decoders are soft-input soft-output decoders. The channelinputs to variable nodes Vi are modeled as soft log-likelihood ratios(LLR).

The methods, systems and devices described herein may apply extrinsicinformation transfer (EXIT) analysis. Extrinsic information transfer(EXIT) analysis is a variant of single parameter Gaussian approximateddensity evolution (GA-DE) analysis. The single parameter used to trackcodeword estimation improvement is Gaussian approximated extrinsicmutual information (MI). Extrinsic information means, that a nodereceives information on its codeword bit which did not originate fromthe very same node. When variable node vi receives incorrect channelinformation L_(ch,i) for the associated bit ĉ_(i), it needs to receivesufficient extrinsic information from its adjacent check

nodes to correct the value of ĉ_(i). The MI between the extrinsicmessages arriving at v_(i) and sent codeword bit c_(i) has to convergeto one for l→∞.

The methods, systems and devices described herein may be implemented inwireless communication networks, in particular communication networksbased on mobile communication standards such as LTE, in particular 4Gand 5G. The methods, systems and devices described below may beimplemented in network nodes, base stations and mobile terminals. Thedescribed devices may include integrated circuits and/or passives andmay be manufactured according to various technologies. For example, thecircuits may be designed as logic integrated circuits, analog integratedcircuits, mixed signal integrated circuits, optical circuits, memorycircuits and/or integrated passives. Target wireless standards for whichthe technology according to this disclosure can be employed are inparticular the following: cellular wide area radio communicationtechnology (which may include e.g. 5th Generation (5G) communicationsystems, a Global System for Mobile Communications (GSM) radiocommunication technology, a General Packet Radio Service (CPRS) radiocommunication technology, an Enhanced Data Rates for GSM Evolution(EDGE) radio communication technology, and/or a Third GenerationPartnership Project (3GPP) radio communication technology (e.g. UMTS(Universal Mobile Telecommunications System), FOMA (Freedom ofMultimedia Access), 3GPP LTE (Long Term Evolution), 3GPP LTE Advanced(Long Term Evolution Advanced)), CDMA2000 (Code division multiple access2000), CDPD (Cellular Digital Packet Data), Mobitex, 3G (ThirdGeneration), CSD (Circuit Switched Data), HSCSD (High-SpeedCircuit-Switched Data), UMTS (3G) (Universal Mobile TelecommunicationsSystem (Third Generation)), W-CDMA (UMTS) (Wideband Code DivisionMultiple Access (Universal Mobile Telecommunications System)), HSPA(High Speed Packet Access), HSDPA (High-Speed Downlink Packet Access),HSUPA (High-Speed Uplink Packet Access), HSPA+(High Speed Packet AccessPlus), UMTS-TDD (Universal Mobile TelecommunicationsSystem—Time-Division Duplex), TD-CDMA (Time Division—Code DivisionMultiple Access), TD-CDMA (Time Division—Synchronous Code DivisionMultiple Access), 3GPP Rel. 8 (Pre-4G) (3rd Generation PartnershipProject Release 8 (Pre-4th Generation)), 3GPP Rel. 9 (3rd GenerationPartnership Project Release 9), 3GPP Rel. 10 (3rd Generation PartnershipProject Release 10), 3GPP Rel. 11 (3rd Generation Partnership ProjectRelease 11), 3GPP Rel. 12 (3rd Generation Partnership Project Release12), 3GPP Rel. 13 (3rd Generation Partnership Project Release 12), 3GPPRel. 14 (3rd Generation Partnership Project Release 12), 3GPP LTE Extra,LTE Licensed-Assisted Access (LAA), UTRA (UMTS Terrestrial RadioAccess), E-UTRA (Evolved UMTS Terrestrial Radio Access), LTE Advanced(4G) (Long Term Evolution Advanced (4th Generation)), cdmaOne (2G),CDMA2000 (3G) (Code division multiple access 2000 (Third generation)),EV-DO (Evolution-Data Optimized or Evolution-Data Only), AMPS (1G)(Advanced Mobile Phone System (1st Generation)), TACS/ETACS (TotalAccess Communication System/Extended Total Access Communication System),D-AMPS (2G) (Digital AMPS (2nd Generation)), PTT (Push-to-talk), MTS(Mobile Telephone System), IMTS (Improved Mobile Telephone System), AMTS(Advanced Mobile Telephone System), OLT (Norwegian for OffentligLandmobil Telefoni, Public Land Mobile Telephony), MTD (Swedishabbreviation for Mobiltelefonisystem D, or Mobile telephony system D),Autotel/PALM (Public Automated Land Mobile), ARP (Finnish forAutoradiopuhelin, “car radio phone”), NMT (Nordic Mobile Telephony),Hicap (High capacity version of NTT (Nippon Telegraph and Telephone)),CDPD (Cellular Digital Packet Data), Mobitex, DataTAC, iDEN (IntegratedDigital Enhanced Network), PDC (Personal Digital Cellular), CSD (CircuitSwitched Data), PHS (Personal Handy-phone System), WiDEN (WidebandIntegrated Digital Enhanced Network), iBurst, Unlicensed Mobile Access(UMA, also referred to as also referred to as 3GPP Generic AccessNetwork, or GAN standard)), Wireless Gigabit Alliance (WiGig) standard,mmWave standards in general (wireless systems operating at 10-90 GHz andabove such as WiGig, IEEE 802.11ad, IEEE 802.11ay, etc.), etc.

The methods and devices described herein may be configured to transmitand/or receive radio signals. Radio signals may be or may include radiofrequency signals radiated by a radio transmitting device (or radiotransmitter or sender) with a radio frequency lying in a range of about3 Hz to 300 GHz. The frequency range may correspond to frequencies ofalternating current electrical signals used to produce and detect radiowaves.

The methods and devices described herein after may be designed inaccordance to mobile communication standards such as e.g. the Long TermEvolution (LTE) standard or the advanced version LTE-A thereof. LTE(Long Term Evolution), marketed as 4G LTE and 5G LTE, is a standard forwireless communication of high-speed data for mobile phones and dataterminals. The technology according to this disclosure can be employedin all systems employing LDPC codes (alone or together with othercodes), such as for example IEEE 802.11n WiFi, IEEE 802.11ac WiFi, IEEE802.16e WiMax, GMR-1, IEEE 802.3an, IEEE 802.22, CMMB, WiMedia 1.5,DVB-S2, Digital Terrestrial Multimedia Broadcast (DTMB), ITU-T G.hn,etc.

The methods and devices described hereinafter may be applied in OFDMsystems. OFDM is a scheme for encoding digital data on multiple carrierfrequencies. A large number of closely spaced orthogonal sub-carriersignals may be used to carry data. Due to the orthogonality of thesub-carriers crosstalk between sub-carriers may be suppressed.

The methods and devices described hereinafter may be applied in MIMOsystems and diversity receivers. Multiple-input multiple-output (MIMO)wireless communication systems employ multiple antennas at thetransmitter and/or at the receiver to increase system capacity and toachieve better quality of service. In spatial multiplexing mode, MIMOsystems may reach higher peak data rates without increasing thebandwidth of the system by transmitting multiple data streams inparallel in the same frequency resources. A diversity receiver uses twoor more antennas to improve the quality and reliability of a wirelesslink.

In the following, embodiments are described with reference to thedrawings, wherein like reference numerals are generally utilized torefer to like elements throughout. In the following description, forpurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of one or more aspects ofembodiments. However, it may be evident to a person skilled in the artthat one or more aspects of the embodiments may be practiced with alesser degree of these specific details. The following description istherefore not to be taken in a limiting sense.

The various aspects summarized may be embodied in various forms. Thefollowing description shows by way of illustration various combinationsand configurations in which the aspects may be practiced. It isunderstood that the described aspects and/or embodiments are merelyexamples, and that other aspects and/or embodiments may be utilized andstructural and functional modifications may be made without departingfrom the scope of the present disclosure.

FIG. 1a is a system diagram illustrating a mobile communication system100 a including different Radio Access Technologies (RATs) 11, 12 forcommunication between a base station 10 and a mobile station 20. Methodsand devices as described in the following sections may be implemented inthe base station 10 and/or the mobile station 20. FIG. 1b is a blockdiagram illustrating a mobile communication system 100 b using jointencoding across multiple Radio Access Technologies (RATs). Both RATs canbe identical, for example the user may operate two WiFis simultaneously.Also, they can be different, e.g. LTE and WiFi may be operatedsimultaneously. A message u is transmitted from a source node S to ajoint encoder 101 encoding the message u with a first subcode C1 and asecond subcode C2. The message u coded with subcode C1 is transmitted bya first transmission path 110, the message u coded with subcode C2 istransmitted by a second transmission path 111. After transmission overthe first transmission path 110, a first codeword Ĉ₁ is received at ajoint decoder 108 and after transmission over the second transmissionpath 111, a second codeword Ĉ₂ is received at the joint decoder 108. Thejoint decoder 108 decodes both received codewords Ĉ₁ and Ĉ₂ and providesan estimate û of the original message u to a destination node D. Thecode comprising sub-codes may be a multi-edge LDPC code. Each sub-codemay correspond to a different Edge-Type that has its specific nodedegree distribution. Due to different node degrees, each of thesub-codes may have different coding characteristics. Note that a “path”may correspond to independent radio standards which are operatedjointly, such as LTE, WiFi, etc. Furthermore, a “path” may relate to anindependent transmission stream, such as independent Spatial Streams inMIMO systems which are generated by a single system but exploit amultitude of independent spatial streams for transmission throughspatial division multiplexing. In such a case, the data on each of thespatial streams can be encoded independently and thus the schemeintroduced in this disclosure can be applied. Obviously, also a mixtureof such spatial division multiplexing schemes with other systems, likeother WiFi flavors, is of course possible.

The first transmission path 110 may be according to a first Radio AccessTechnology, e.g. including an OFDM modulator 102, a fading channel 104,for example smaller than 6 GHz and an OFDM demodulator 107. The secondtransmission path 111 may be according to a second Radio AccessTechnology, e.g. including an OFDM modulator 103, a fading channel 105,for a millimeter wave fading channel and an OFDM demodulator 106.Strictly speaking mmWave bands start at 30 GHz. In the context of thisdisclosure, however, the term “mmWave spectrum” is used for bands above6 GHz. Bands below 6 GHz are considered to be legacy wireless broadbandbands.

The mobile communication system 100 b includes a first transmission path110, a second transmission path 111 and an encoder 101. The firsttransmission path 110 is configured to transmit a message u according toa first radio access technology 104. The second transmission path 111 isconfigured to transmit the message u according to a second radio accesstechnology 105. The encoder 101 is configured to encode the message u bya code before transmission of the message u over the first transmissionpath 110 and the second transmission path 111. The code includes aplurality of subcodes c1, c2. The encoder 101 is configured to encodethe message u intended for transmission over the first transmission path110 with a first subcode c1 of the plurality of subcodes c1, c2 and toencode the message u intended for transmission over the secondtransmission path 111 with a second subcode c2 of the plurality ofsubcodes c1, c2.

The first radio access technology 104 may be a technology which employsLDPC coding, such as for example IEEE 802.11ac WiFi or 802.11n, etc. Thesecond radio access technology 105 may be for example a millimeter waveradio access technology. Existing channel codes defined in therespective standards may be replaced by a joint multi-Edge LDPC code asmentioned above. For example, through a horizontal standard, existingcodes can be replaced by multi-edge codes of identical coding rates,block size, etc. Then, the remaining part of the TX chain remainsunchanged. In the RX chain a joint decoder may be implemented.

The code may include a multi-edge low density parity check (LDPC) codeincluding a plurality of multi-edge LDPC subcodes. The first subcode c1may include a first multi-edge LDPC subcode of the plurality ofmulti-edge LDPC subcodes. The second subcode c2 may include a secondmulti-edge LDPC subcode of the plurality of multi-edge LDPC subcodes.Each subcode may correspond to a different edge-type having a specificnode degree distribution. A subcode corresponds to the sub-part of aTanner-Graph of a given Edge Type (e.g., all connected “B” edges in FIG.5a or similar). Alternatively, a subcode may correspond to a sub-part ofthe Tanner Graph of multiple Edge Type (e.g., all connected “B” and “R”edges in FIG. 5a or similar).

The first multi-edge LDPC subcode may be based on a signal tointerference and noise characteristic of the first transmission path110. The second multi-edge LDPC subcode may be based on a signal tointerference and noise characteristic of the second transmission path111. The first subcode and the second subcode may be optimized withrespect to an observed multi-path propagation profile. The LDPC sub-codewas derived such that it is beneficial characteristics for conveyingmessages over channels of certain characteristics, for example it may beoptimized for a given multipath propagation profile, a certain SINR,etc. The corresponding optimization result typically leads to a certainnode degree distribution (i.e., how many connection may arrive/departfrom a given node).

The message u may include a plurality of data containers, also called“resource blocks” in an LTE specific notation. The first multi-edge LDPCsubcode may be allocated to a first data container of the plurality ofdata containers and the second multi-edge LDPC subcode may be allocatedto a second data container of the plurality of data containers. The term“resource block” as such is mainly used for LTE. Methods, systems anddevices according to the disclosure, however, may apply to any othertype of data container as well.

The allocation of the first multi-edge LDPC subcode to the first datacontainer may be based on a signal-to-interference and noise ratio(SINR) of the first data container. The allocation of the secondmulti-edge LDPC subcode to the second data container may be based on asignal-to-interference and noise ratio (SINR) of the second datacontainer. The first subcode and the second subcode may be optimizedwith respect to an observed multi-path propagation channel.

The first multi-edge LDPC subcode may be designed for allocation to adata container of the plurality of data containers on which an SINR isabove a first SINR level. The second multi-edge LDPC subcode may bedesigned for allocation to a data container of the plurality of datacontainers on which an SINR is below a second SINR level.

In particular, a 5G wireless mobile communication system is consideredin FIG. 1b in which multiple Radio Access Technologies (RATs) areoperated simultaneously, for example 3GPP LTE (or its Evolution towards5G) 104 and millimeter wave RATs 105. In this context it is assumed thata single code will be employed for the joint encoding 101 acrossmultiple RATs as it is illustrated in FIG. 1b for the example ofcombining a millimeter wave and a <6 GHz RAT. It is understood thatinstead of these RATs depicted in FIG. 1b , other RATs may be used aswell.

In the mobile communication system 100 b, Multi-EdgeLow-Density-Parity-Check (LDPC) Coding may be employed as described inthe following since it allows to optimize sub-codes for distinctchannels and to still keep the advantages of one overall code for jointencoding across all applicable RATs.

Typically, the presence of training sequences is known in advance.Therefore, in the following sections it is demonstrated how toparameterize the Multi-Edge LDPC code in order to optimally addressResource Blocks of different SINR. This is possible since a Multi-EdgeLDPC code consists of multiple sub-Codes which can be optimizedindependently and thus they can be optimally mapped onto sequences ofdifferent SINR (one sub-Code being optimized for one given SINR level).

The basic principle is to derive a “Multi-Edge Low Density Parity Check(LDPC)” Code comprising a multitude of “Edge-Types”, e.g. according tothe description of FIGS. 2 and 5 a/b. For each of the edge-types acorresponding sub-Code structure may be identified which may beoptimized for a different SINR level.

Resource Blocks of an LTE system may be identified in which interferenceis known to be introduced by neighboring cell(s), typically thoseresource blocks are training sequence blocks. The content of the LTEResource Blocks of a given Serving Cell may be processed by a channelcode which is corresponding to an optimum sub-Code of a Multi-Edge LDPCCode being optimized for the observed SINR level as described in thefollowing sections.

The disclosure describes a solution to select an optimized Multi-EdgeLDPC sub-code which is specifically optimized for the given SINR levelsof a given Resource Block. By using such Multi-Edge LDPC sub-codes asubstantial coding gain can be achieved compared to the state of the artsolutions which apply an identical underlying code onto all ResourceBlocks.

The parameterization of the Multi-Edge LDPC Code may be communicatedthrough a standardized interface.

FIG. 2 is a schematic diagram illustrating Multi-Edge LDPC (Low DensityParity Check) coding 200. The Multi-Edge LDPC code 200 uses a pluralityof check nodes 201, 202, 203 that are connected by multiple edge typesT1, T2 to a plurality of variable nodes 211, 212, 213, 214 which mayserve as arbitrary channel inputs.

Multi-Edge LDPC codes are a generalization of Single-Edge type LDPCcodes. This generalization allows specifying more special LDPC codesthan regular and irregular LDPC codes like repeat-accumulating, cyclic,and protograph based LDPC codes. The Multi-Edge framework introduces newconstraints to specify the graph of LDPC codes. These Multi-Edgeconstrained graphs allow the design of single LDPC codes with highlydifferent behavior for different parts of the codes, e.g. the sub-codes.

This is a key property of Multi-Edge LDPC codes. Those different partscan be exploited in order to optimize sub-codes (together with theoverall code) for sub-channels with distinct channel gain properties. Inthe extreme case where each edge in the graph has a unique edge-type anindividual LDPC code may be specified.

The following constraints are introduced by the framework: Multipleedge-types are introduced into the graph which enables the guidance ofthe interconnection between codeword bits and associated parity checkequations; Each codeword bit can be assigned an arbitrary channel input.To specify punctured codeword bits the channel input value zero can beassigned.

Various performance aspects of LDPC codes like threshold performance,error floor levels and decoding complexity can be optimized through thisframework. The threshold performance of Multi-Edge LDPC codes isassessable through Multi-Edge density evolution and through Multi-EdgeEXIT (Extrinsic Information Transfer) analysis. Error floor levels maydepend on the interconnection between codeword bits and associatedparity check equations. Decoding complexity may be dependent on thenumber of interconnections per codeword bit/parity check equation.

The new approach according to the disclosure is structured as follows:A) Identification of SINR levels across LTE Resource Blocks of a ServingCell; B) Allocation of suitable Multi-Edge LDPC Sub-Codes to variousResource Blocks depending on SINR levels; and C) Encoding of data andtransmission.

With respect to Identification of SINR levels across LTE Resource Blocksof a Serving Cell, a Multi-Edge LDPC matrix may be derived which isproviding a multitude of “Edges Types”, each of which is leading asub-LDPC Code that is optimized for a given SINR level and channelprofile. For this reason, a step function may be derived representing astatistical distribution of the channel gains of a given RAT in aquantized representation. The basic principle is indicated in FIGS. 3and 4 below.

FIG. 3 is a graph 300 illustrating an exemplary probability densityfunction over channel gains. The probability density function representsthe probability density for different quantization steps #1, #2, #3 and#4 related to the step limits q1, q2, q3 etc. as depicted in FIG. 3.

FIG. 4 is a graph 400 illustrating an exemplary quantized representationof channel gains. The quantized absolute value of channel gains q1, q2,q3 etc. can be represented as a step function as depicted in FIG. 4.

FIG. 5a is a schematic diagram illustrating an exemplary LDPC code 500 awith various Edge Types (in this Example with Edge Types G, B and R).

The Multi-Edge LDPC code 500 a uses a plurality of check nodes 501, 502,503 that are connected by multiple edge types G (edge type green), B(edge type blue) and R (edge type red) to a plurality of variable nodes511, 512, 513 which may serve as arbitrary channel inputs. Only aspecific edge type is connected to a corresponding edge type, i.e. edgetype G is connected to edge type G, edge type B is connected to edgetype B and edge type R is connected to edge type R.

The illustration shows how multiple Edges are managed in the context ofa Multi-Edge LDPC code such that each corresponding sub-Code can beoptimized for a different quantized channel gain.

FIG. 5b is a schematic diagram illustrating an exemplary LDPC code 500 bwith various Edge Types (in this Example with Edge Types G and R).

The Multi-Edge LDPC code 500 b correspond to the Multi-Edge LDPC code500 a from which the edge types B are removed. I.e., edge type G isconnected to edge type G and edge type R is connected to edge type R.

The multi-edge type framework has two novel properties which aredistinguishable edges and sockets and variable nodes associated withdifferent channel outputs. These properties may be used to design LDPCcodes optimized for multiple channel inputs. The socket concept of thesingle-edge type setting is extended in the multi-edge type framework.Every multi-edge type socket is linked to an edge type. The multi-edgetype edge interleaver is constrained to only connect sockets of the sameedge type. An example of the multi-edge type edge interleaver and theassociation of variable nodes to different channel inputs is shown inthe FIGS. 5a and 5b . The two socket types are differentiated by colorsGreen (G), Blue (B) and red (R) in FIG. 5a and by colors Green (G), andred (R) in FIG. 5 b.

For single-edge type degree distributions, scalar values suffice todenote the degree of a node. In contrast, for multi-edge type degreedistributions a degree type vector is needed. Similar to single-edgetype degree distributions, the degree values are denoted as exponents.The degree type vector dc=(d(1); : : : ; d(T)) for check nodes has Tinteger entries. The ith value in the vector represents the number ofedges or sockets of the ith edge type.

In the following, the model described above is applied to a LTE scenarioin which neighboring cell(s) create(s) interference onto a serving cellas it is illustrated in the sequel.

FIG. 6 is a schematic diagram illustrating an exemplary Multi-Edge LDPCsub-Code mapping 600 onto Resource Blocks not undergoing interferencefrom neighboring cells. FIG. 6 represents a resource block in atime/frequency representation where l=0, l=6 and l=13 specifies the timeor subframe index while k=0 and k=11 specifies the frequency index.

Reference signals of the serving cell are denoted by R0, R1, R2 and R3while cell-specific reference signals CRS with individual offsets,colliding and non-colliding with serving cell CRS are represented by R4,R5, R6, . . . , R51. Interference is always present regardless ofnetwork load.

FIG. 6 shows allocation of resource blocks 601, 602, 603, 604 to thoseMulti-Edge LDPC sub-codes which have been optimized for high SINR levels(since no interference is expected from neighboring cells). Theselection of LDPC sub-codes also takes the channel gain onto the variousresource blocks into account.

FIG. 7 is a schematic diagram illustrating an exemplary Multi-Edge LDPCsub-Code mapping 700 onto Resource Blocks undergoing interference fromneighboring cells. FIG. 7 represents a resource block in atime/frequency representation where l=0, l=6 and l=13 specifies the timeor subframe index while k=0 and k=11 specifies the frequency index.

Reference signals of the serving cell are denoted by R0, R1, R2 and R3while cell-specific reference signals CRS with individual offsets,colliding and non-colliding with serving cell CRS are represented by R4,R5, R6, . . . , R51 according to the representation of FIG. 6.

FIG. 7 shows allocation of resource blocks 701, 702, 703, 704 to thoseMulti-Edge LDPC sub-codes which have been optimized for low SINR levels(since interference is expected from neighboring cells). The selectionof LDPC sub-codes also takes the channel gain onto the various resourceblocks into account.

As described above, the new approach according to the disclosure isstructured as follows: A) Identification of SINR levels across LTEResource Blocks of a Serving Cell; B) Allocation of suitable Multi-EdgeLDPC Sub-Codes to various Resource Blocks depending on SINR levels; andC) Encoding of data and transmission.

With respect to the first step A), knowledge may be identified that isavailable on Interference levels (created by Neighboring Cells) onto theResource Blocks of the Serving Cell in future transmissions. This istypically knowledge on Reference Signals to be transmitted byInterfering Cells in the future on specific Resource Blocks. In somecases, it may also be knowledge on the usage (or non-usage) of ResourceBlocks to be used for data transmission in the future by interferingcells. For example, statistical models (such as Bayesian prediction) canbe exploited in order to predict the future allocation of data symbolsto Resource Blocks in Neighboring Cells. Direct interactions between aServing Cell and Neighboring Cells may be applied as well, i.e. theinformation on future allocation of data to Resource Blocks may bedirectly communicated by a Neighboring Cell to a Serving Cell. Thelatter case may in particular be applicable to Cloud-RAN types ofnetworks where all signal processing of multiple (all) Base Stations isperformed in a single server farm.

With respect to the second step B), the fact can be exploited that thereis a 1:1 relationship between the quantized channel gains illustratedabove at modified SINR levels. Actually, a higher channel attenuationfinally just leads to an decreased SINR level (since the signal level islower while the Interference and Noise levels typically are assumed tobe constant over the entire spectrum); a lower channel attenuationfinally leads to an increased SINR level (since the signal level ishigher while the Interference and Noise levels typically are assumed tobe constant over the entire spectrum). So, when the channel gain doesnot change but the Interference levels vary, the resulting configurationcan be directly mapped to the case in which the channel gain varies andthe Interference level stays constant (an increase/decrease of theinterference level of “x dB” corresponds to an increase/decrease of thechannel attenuation by the same “x dB” with the interference plus noiselevels being assumed to be constant). With this observation, thesuitable sub-Code of the given Multi-Edge LDPC Code may be allocated toeach of the Resource Blocks, i.e. a sub-Code being optimized for highSINR levels may be allocated to those Resource Blocks where nointerference is expected from neighboring cells and a sub-Code beingoptimized for low SINR levels may be allocated to those Resource Blockswhere interference is expected from neighboring cells.

Then, the third step C) can be applied, i.e. encoding of data andtransmission. The user data may be encoded by the LDPC according to themapping identified above, e.g. as it is illustrated in the examples ofFIG. 6 and FIG. 7. The applied encoding selection may be communicated tothe receiver through proper signaling.

For the MIMO case, the usage of different Multi-Edge LDPC sub-codes andtheir allocation to Resource Blocks is illustrated in FIGS. 8a and 8 b.

FIG. 8a is a schematic diagram illustrating allocation of resourceblocks 800 a for a MIMO Reference Signal scenario.

FIG. 8a represents a resource block in a time/frequency representationwhere l=0, l=6 and l=13 specifies the time or subframe index while k=0and k=11 specifies the frequency index for a MIMO scenario.

Reference signals of the serving cell are denoted by R0, R1, R2 and R3while UE-specific demodulation reference signals DMRS are represented byD1 and D2. UE-specific demodulation reference signals (DMRS) allow forexample up to 8 layer multi-user MIMO transmission. No explicitprecoding signaling is required, as DMRS undergo the same precoding asthe data.

Resource blocks 801 may be allocated to those multi-edge LDPC sub-codeswhich have been optimized for high SINR levels (since no interference isexpected from neighboring cells). The selection of LDPC sub-codes mayalso take the channel gain onto the various resource blocks intoaccount.

FIG. 8b is a schematic diagram illustrating allocation of resourceblocks 800 b for a MIMO Reference Signal scenario with Channel StateInformation (CSI) Reference Signals.

FIG. 8b represents a resource block in a time/frequency representationwhere l=0, l=6 and l=13 specifies the time or subframe index while k=0and k=11 specifies the frequency index for a MIMO scenario with channelstate information reference signals.

Reference signals of the serving cell are denoted by R0, R1, R2 and R3while UE-specific demodulation reference signals DMRS are represented byD1 and D2 according to the representation of FIG. 8a . Sporadicsubframes with channel state information reference signals (CSI-RS)allow channel state feedback calculation for eNB link adaptation.

Resource blocks 802 may be allocated to those multi-edge LDPC sub-codeswhich have been optimized for low SINR levels (since interference isexpected from neighboring cells). The selection of LDPC sub-codes mayalso take the channel gain onto the various resource blocks intoaccount.

Another application for allocation of multi-edge LDPC sub-codes is inHetNets where the Pico Cell receives the Macro Cell signals but not viceversa. The Pico cell may estimate the interference for future TTIs andallocate Multi-Edge LDPC Sub-Codes according to these estimates (i.e.Resource blocks allocated to Multi-Edge LDPC Sub-Codes optimized forhigh SINR levels when Interference from Macro Cell is high and Resourceblocks allocated to Multi-Edge LDPC Sub-Codes optimized for low SINRlevels when Interference from Macro Cell is low).

Another application for allocation of multi-edge LDPC sub-codes is inAlmost Blank Subframes (ABS) for enhanced ICIC (eICIC) where the ABS forthe Macro Cell are transmitted with a semi static pattern and thepattern is known at the small cells within the range of the Macro Cell.The small cell allocates Resource Blocks to a UE at the small cell edgeand encodes them with Multi-Edge LDPC Sub-Codes optimized for the SINRlevels corresponding to whether the Macro inserts ABS at these positionsin the radio frame and Interference is low or no ABS is inserted andInterference is high.

The new approach according to the disclosure may described as follows:Identification of different SINR levels on LTE Resource Blocks (due tointerference from neighbouring cells, the SINR levels may be reduced)and allocation of suitable Multi-Edge LDPC sub-codes to LTE Resourceblocks of suitable SINR. I.e., each Multi-Edge LDPC sub-code has beenoptimized for a given SINR level and the suitably optimized sub-code ismapped to those Resource Blocks on which the target SINR level isobserved. For UL, Mobile Device may be informed by BS about the suitableMulti-Edge LDPC sub-code to be used for the various Resource Blocks. TheMobile Device may apply the indicated Multi-Edge LDPC subcode such thatthe data is suitably encoded for the target Resource Blocks.

FIG. 9 is a block diagram illustrating a mobile receiver 900 detectingmultiple interference scenarios by decoding with multiple subcodes.

The mobile receiver 900 includes a receiving circuit 901 and a detectioncircuit 902. The receiving circuit 901 is configured to receive a signal911 comprising transmissions 910 from a serving radio cell 920 and aplurality of interfering radio cells 921 a, 921 b, wherein the signal(911) is encoded by a code comprising a plurality of subcodes. Thedetection circuit 902 is configured to detect a first interferencescenario 912 based on decoding the signal 911 with a first subcode ofthe plurality of subcodes and to detect a second interference scenario913 based on decoding the signal 911 with a second subcode of theplurality of subcodes.

The receiving circuit 901 may be configured to receive the signal 911including a plurality of resource blocks, wherein a particular subcodeof the plurality of subcodes may be allocated to a respective resourceblock of the plurality of resource blocks. The particular subcodeallocated to the respective resource block may depend on a channel gainof the respective resource block.

The first subcode may be adapted or optimized for the first interferencescenario 912 and the second subcode may be adapted or optimized for thesecond interference scenario 913.

The code may include a multi-edge low density parity check (LDPC) codeincluding a plurality of multi-edge LDPC subcodes as described abovewith respect to FIGS. 1 to 8.

FIG. 10 schematically illustrates an exemplary method 1000 forgenerating a super LDPC code.

For a given combination of RATs and the given characteristics (inparticular frequency domain channel gain profile) of the over-the-airpropagation channels for each RAT, a different optimum Multi-Edge LDPCCode needs to be found. The disclosed method 1000 is a solution to theproblem on how to assembly a (near) optimum code in an efficient andlow-complexity way while relying on pre-calculated structures (allowinga highly efficient hardware implementation).

The basic principle is to derive a “Multi-Edge Low Density Parity Check(LDPC)” Code comprising a multitude of “Edges” (possibly tens of edgesor even hundreds of edges or more). For each of the edges acorresponding sub-Code is identified. For given set of RATs and a givenobservation of the over-the-air propagation channel characteristics, themost suitable sub-Codes out of the original “super LDPC matrix” areextracted and assembled into a new LDPC matrix which is finally used forthe encoding/decoding approach.

Known solutions typically rely on independently derived codes for eachof the concerned RATs. The disadvantage is that the application ofindependent codes do not allow for an exploitation of coding diversityacross multiple RATs. The solution according to the method 1000 asdescribed in the following allows to have an easy, low-complex andstraight forward selection of sub-codes from a “super LDPC matrix” andto assemble a new code (comprised of the selected sub-codes) whichadditionally allows for an exploitation of code diversity across theconcerned RATs.

The method 1000 may be used for generating a super low density paritycheck (LDPC) code based on an LDPC code including a first set ofvariable nodes and a second set of check nodes. The wording “super”relates to the fact that a large number of different sub-codes areintegrated into a single large matrix.

The method 1000 includes generating 1001 a plurality of edge types forthe first set of variable nodes and the second set of check nodes, eachedge type depending on a particular radio access technology of a set ofradio access technologies and a channel profile of the particular radioaccess technology. The method 1000 includes connecting 1002 a particularedge type of the variable nodes with a corresponding edge type of thecheck nodes according to a particular subcode of the LDPC code. Themethod 1000 includes generating 1003 the super low density parity check(LDPC) code based on the edge type connections of the plurality of edgetypes.

The particular subcode of the LDPC code may be optimized for theparticular radio access technology and the channel profile of theparticular radio access technology.

The method 1000 may include: generating the channel profile of theparticular radio access technology as a step function representing astatistical distribution of channel gains of the particular radio accesstechnology in a quantized representation.

The method 1000 may include: identifying the set of radio accesstechnologies based on radio access technologies that can be combined ina transmitter/receiver pair and based on a required link characteristicof the transmitter/receiver pair.

The LDPC code may include a multi-edge LDPC code including a pluralityof multi-edge LDPC subcodes, e.g. as described above with respect toFIGS. 1 to 8. A user may operate “N” different RATs simultaneously (or(partly) sequentially) and multi-Edge LDPC coding is used for a subsetof “K<N” of those RATs. The other RATs may use other channel codingschemes which are defined in the respective standards. In oneimplementation form existing coding schemes (for example Turbo Codedefined in LTE, legacy LDPC codes in IEEE 802.11n WiFi, etc.) may beimplemented by multi-Edge LDPC codes in order to achieve an improvedsystem performance in the context of a multi-RAT context. Such anapproach can for example be achieved through a “horizontal” standardwhich defines that the existing coding schemes need to be replaced. Insuch a case, the code rate (and block size, etc.) of the correspondingmulti-edge LDPC sub-codes must exactly correspond to the code rate (andblock size, etc.) of the channel codes defined in the original standardsuch that the remaining part of the transmission chain is untouched. Inthe receiver, a joint channel decoder must then be implemented decodingall multi-Edge encoded data streams over all RATs. This finally leads toan improved system performance due to the exploitation of synergiesacross multiple RATs. This approach of replacing existing channel codingschemes by another “joint” scheme across multiple RATs can of course beapplied to other channel codes (other than multi-edge LDPC codes) suchas a joint convolutional code, joint single-edge LDPC code, joint TurboCode, joint Block Code, etc.

The method 1000 may include: generating 1003 the super low densityparity check (LDPC) code as a matrix comprising all edge typeconnections of the plurality of edge types.

In the following, a presentation for generating a super LDPC matrix isgiven. The presentation of the new approach is structured as follows: i)Derivation of a “super LDPC matrix” comprising a multitude of“Edge-Types” (possibly tens of “edge-types” or even hundreds of“edge-types” or more) optimized for a large set of different RATs andtheir over-the-air propagation channel characteristics. ii) Selection ofthe suitable combination of RATs in the transmitter and observation ofover-the-air propagation channel characteristics. iii) Selection of thesuitable sub-codes (i.e., related to a given “Edge-Type” in the LDPCcode) and combination of the identified sub-codes into a new joint LDPCCode Matrix. Derivation of a “super LDPC matrix” comprising a multitudeof “Edges” (possibly tens of edges or even hundreds of edges or more)optimized for a large set of different RATs and their over-the-airpropagation channel characteristics.

The “super LDPC matrix” is providing a multitude of “Edges Types”, eachof which is leading a sub-LDPC Code that is optimized for a given RATand a given over-the-air channel profile. For this reason, a stepfunction may be derived representing a statistical distribution of thechannel gains of a given RAT in a quantized representation.

For each RAT and each quantized channel gain level illustrated abovewith respect to FIGS. 3 and 4, a distinct Edge Type may be introducedinto the LDPC optimization process. The code may then be derived in thefollowing steps: 1. Derive an (optimum) ensemble of Edge Type degreedistribution for Variable nodes and Check nodes of the LDPC Code, 2.Derive a suitable Code meeting the Edge Type degree distributionrequirements derived in item 1.

Based on the available RATs that can be combined in a giventransmitter/receiver pair (or ensemble of (multiple) transmitter(s) and(multiple) receiver(s)) and based on the required link characteristics(such as system latency, aggregate throughput, BER/PER requirements,subscription cost requirements, or a combination of such requirements,etc.) an ensemble of RATs to be operated simultaneously may beidentified. In a typical example, LTE is jointly operated in combinationwith WiFi, or LTE is jointly operated in combination with WiGig, etc.Once the combination of RATS is selected, the propagation channel isobserved, e.g. through channel impulse response measurements (possiblythrough feedback by the receiver, or through exploitation of reciprocityof transmission/reception paths, etc.) or similar.

In step i) above, as illustrated in FIG. 4, for each RAT a set ofsub-Codes may be defined each corresponding to a given propagationchannel quantization level. For the observed actual over-the-airpropagation channel, the most suitable quantization levels to beconsidered may be derived (among the pre-defined ensemble—it may bedefined as illustrated in FIG. 4 for a given example).

Once the set of most suitable quantization levels for a givenover-the-air propagation channel is identified, the correspondingsub-Codes may be identified (this is predefined and typically onlyrequires to check a look-up table, i.e. typically when the super LDPCmatrix is provided, there is information given which sub-Code (and thuswhich Edge-Type) is related to which RAT and which channel quantizationlevel).

Once the suitable sub-Codes (and thus applicable Edge-Types) areidentified, those may be subtracted from the super LDPC Matrix andaggregated into a new LDPC encoding matrix. Suitable sub-Codes may beidentified by matching Edge-Type degree to the required linkcharacteristics (such as system latency, aggregate throughput, BER/PERrequirements, subscription cost requirements, or a combination of suchrequirements, etc.) by either 1. Analytical information: high SNR linkusually corresponds to large Edge-Type degrees or 2. Empiricalinformation: testing each sub-Code with each link characteristic andmeasuring its performance (e.g. BER, FER, Coding Gain etc). The superLDPC Matrix encodes the Edge-Type corresponding to each edge (e.g. 1 forEdge-Type-1, 2 for Edge-Type-2 etc.). The reduced LDPC Matrix may bederived by either 1. Copying the super LDPC Matrix and deleting allEdge-Types of non-suitable sub-codes or 2. Copying all suitableEdge-Types into an empty (e.g. all-zero) LDPC matrix of the same size.From the reduced LDPC Matrix any hardware/software implementation forencoding/decoding can be derived.

Assuming that the super LDPC Matrix corresponding to the graph given inFIG. 5 is used, and assuming that only sub-Codes corresponding to EdgeTypes 1 and 3 will be kept, the final LDPC matrix will correspond to thegraph depicted in FIG. 5 b.

An exemplary method for generating a super LDPC code may be defined asfollows: Generation of a large “super” Multi-Edge LDPC code comprising amultitude of distinct sub-codes, each of which is optimized for adifferent SINR level and corresponding propagation channelcharacteristics. Identification of required SINR levels for Multi-EdgeLDPC subcodes (typically by evaluating channel gain and noisecharacteristics in the receiver). Selection of suitable Multi-Edge LDPCsubcodes which are available in the “super” Multi-Edge LDPC code.Extraction of the identified Multi-Edge LDPC sub-codes into a new LDPCmatrix to be applied for channel coding. In the transmitter (eitherMobile Device or Base Station), the resulting Multi-Edge LDPC Matrix isused for channel coding. The coded bits are finally mapped onto thoseResource Blocks/Carriers whose characteristics (noise/propagationchannel characteristics) correspond to the optimization objectives forthe respective Multi-Edge LDPC subcodes. The transmitter (either theMobile Device or Base Station) first sends a training sequence to thereceiver (either Base Station or Mobile Device), then the receiverevaluates the channel and noise characteristics and derives the optimumselection of sub-codes of the “super” Multi-Edge LDPC code to be usedfor the transmission. The receiver informs the transmitter throughsignaling about the preferred Multi-Edge LDPC Matrix composition. Then,the transmitter encodes the information bits and maps them onto theResource Blocks and/or Carriers following the indications by thereceiver.

FIG. 11 schematically illustrates an exemplary method 1100 forgenerating a Multi-Edge LDPC code for encoding a message in a mobilecommunication system.

The method 1100 includes generating 1101 a first set of variable nodesand a second set of check nodes. The method 1100 includes generating1102 a plurality of edge types for the first set of variable nodes andthe second set of check nodes. The method 1100 includes connecting 1103an edge type of the variable nodes with a corresponding edge type of thecheck nodes according to a predetermined metric which is based onextrinsic mutual information exchanged between the first set of variablenodes and the second set of check nodes on each edge type.

The method 1100 may further include deriving the extrinsic mutualinformation based on edge-perspective degree distributions for each edgetype.

The method 1100 may further include deriving the edge-perspective degreedistributions for each edge type by partial derivation of nodeperspective distributions.

The method 1100 may further include deriving the extrinsic mutualinformation based on extrinsic information exchange (EXIT) charts. Thepredetermined metric may be configured to minimize the extrinsic mutualinformation for each edge type.

The method 1100 may further include encoding the message by themulti-edge LDPC code; and transmitting the encoded message over a firsttransmission path according to a first radio access technology and asecond transmission path according to a second radio access technology.

Examples of extrinsic mutual information are described below, inparticular with respect to FIGS. 12 and 13. A metric based on mutualinformation is described in the following sections.

LDPC codes with inherent structure in their associated graph are moreoptimal than random LDPC codes without any structure. The method 1100according to the disclosure addresses the problem on how to designstructured LDPC codes optimal for a multitude of channels withoptimization method of low computational complexity relying onMulti-Edge-Type LDPC codes.

The method 1100 according to FIG. 11 may be used for deriving anoptimized Multi-Edge LDPC ensemble of Tanner Graphs for differentpropagation channels and noise levels. The optimality can be achieved inthe sense of threshold (here, the threshold corresponds to the distanceof the start of the waterfall region from the theoretical capacity foriterative decoding) maximization.

The following presentation of an exemplary method for generating aMulti-Edge LDPC code according to the disclosure addresses threeaspects: For threshold analysis, an optimized novel method called“Multi-Edge Type EXIT Analysis” is introduced. The alternative forevaluating thresholds would be Density Evolution and PEXIT analysisconstraint to protographs. The disadvantage of the PEXIT analysis isthat it can be used only for protographs and not for general Multi-EdgeType LDPC structures. Density Evolution can theoretically be used, it ishowever extremely costly in terms of calculation power requirements andthus often impractical.

Second, a novel way of integrating a differential evolutionaryoptimization algorithm into the upper method for Multi-Edge LDPCthreshold optimization is introduced. Existing methods do not allow tobe applied for fully parameterized Multi-Edge LDPC thresholdoptimization and thus the disclosed approach is the only existing validmethod known to the authors of this disclosure. In literature, theproblem is often reduced to classical single-edge type LDPC codes,however this is sub-optimum.

Third, a solution is introduced for transforming the Multi-Edge Typedegree distribution to a specific Multi-Edge Type LDPC code throughrandom interleaving. This solution maintains the structure of the degreedistribution derived from the optimization stage.

The disclosed solution, in combination with a heuristic optimizationalgorithm, allows structuring the LDPC codes in an optimal way formultiple RATs fully exploiting the Multi-Edge-Type framework.

Multi-Edge LDPC codes are a generalization of Single-Edge type LDPCcodes. This generalization allows specifying more special LDPC codesthan regular and irregular LDPC codes like repeat-accumulating, cyclic,and protograph based LDPC codes. The Multi-Edge framework introduces newconstraints to specify the graph of LDPC codes. These Multi-Edgeconstrained graphs allow the design of single LDPC codes with highlydifferent behavior for different parts of the codes e.g. the sub-codes.

This is a key property of the method 1100 according to the disclosure.Those different parts are exploited in order to optimize sub-codes(together with the overall code) for sub-channels with distinct channelgain properties.

In the extreme case where each edge in the graph has a unique edge-typean individual LDPC code is specified. The following constraints areintroduced by the framework: Multiple edge-types are introduced into thegraph which enables the guidance of the interconnection between codewordbits and associated parity check equations. Each codeword bit can beassigned an arbitrary channel input. To specify punctured codeword bitsthe channel input value zero can be assigned.

Various performance aspects of LDPC codes like threshold performance,error floor levels and decoding complexity can be optimized through thisframework. The threshold performance of Multi-Edge LDPC codes isassessable through Multi-Edge density evolution and through Multi-EdgeEXIT (Extrinsic Information Transfer) analysis. Error floor levelsdepend on the interconnection between codeword bits and associatedparity check equations. Decoding complexity is dependent on the numberof interconnections per codeword bit/parity check equation.

The presentation of the new approach is structured as follows:Evaluation of Multi-Edge-Type degree distributions through theintroduced Multi-Edge-Type EXIT chart analysis; Optimization ofMulti-Edge-Type degree distributions through an optimized heuristic,evolutionary algorithm with the following process inputs and outputs.

The Inputs are: Target number of Edge-Types and target number ofChannels, Target SNR for each of the channels, Bit distribution andTarget Code Rate. Target number of Edge-Types and target number ofChannels may be e.g., 2 channels with different SNR characteristics.Typically, the target number of Edge-Types and target number of Channelsare equal, i.e. there is one Edge-Type for each Channel Type. Target SNRfor each of the channels may be e.g., 0.5 dB & 1.5 dB. Bit distributionmay be e.g. 40% of bits on channel 1 and 60% of bits on channel 2. Notethat the total number of bits does not need to be given in the nodedistribution optimization phase.

The Expected Outputs are Degree Distribution (in form of a discreteprobability density function) for each of the variable nodes and paritycheck nodes for each of the Edge Types.

An Example of the optimization is given in the following: Variable nodedistribution L=0.5r₁x₁ ⁷x₂ ³+0.5r₂x₂ ¹⁰ and Check node distributionR=0.5x₁ ⁷x₂ ¹³ (Note that the sum of weights is typically <1 for CheckNodes, in the example of a factor “0.5”, the number of Check Nodescorresponds to half of the number of Variable Nodes). In this example,half of the variable nodes have degree 7 of Edge Type 1 combined withdegree 3 of Edge Type 2 and are associated with channel-1. The otherhalve of the variable nodes has degree 10 of Edge Type 2 and areassociated with channel-2.

Furthermore, it is described how a specific Multi-Edge LDPC Code isderived based on the Degree Optimization procedure.

Details are given in the sequel.

The iterative decoding threshold value of MET ensembles (i.e. thechannel SNR for which a LDPC code under iterative decoding enters itswaterfall region) may be computed by tracking the extrinsic mutualinformation exchanged between variable nodes and check nodes on eachedge-type.

As a first step the edge-perspective degree distributions λ and p foreach edge-type may be derived from the node-perspective distributions Land R by special partial derivation of L and R of each edge-type, wherethe exponent of the derived edge-type is not decreased by one. Genericexamples for λ and p are given in the following tables:

TABLE 3.1 Generic MET Parameters for λ^((k)) λ_(d) _(v1) ^((k)) r^(b) ¹d₁ ⁽¹⁾ d₁ ⁽²⁾ . . . d₁ ^((T)) λ_(d) _(v2) ^((k)) r^(b) ² d₂ ⁽¹⁾ d₂ ⁽²⁾ .. . d₂ ^((T)) . . . . . . . . . . . . . . . . . .

d_(n) _(v) ⁽¹⁾ d_(n) _(v) ⁽²⁾ . . . d_(n) _(v) ^((T))

TABLE 3.2 Generic MET Parameters for ρ^((k)) ρ_(d) _(c1) ^((k)) d₁ ⁽¹⁾d₁ ⁽²⁾ . . . d₁ ^((T)) ρ_(d) _(c2) ^((k)) d₂ ⁽¹⁾ d₂ ⁽²⁾ . . . d₂ ^((T)). . . . . . . . . . . . . . .  

  d_(n) _(c) ⁽¹⁾ d_(n) _(c) ⁽²⁾ . . . d_(n) _(c) ^((T))

λ and p are the discrete probability function values for each nodedegree type. r are the associated channel inputs and d are the degreesfor each edge-type for the node degree types.

The following equations can be used to track the Gaussian approximateddensities of the messages which are exchanged under iterative decoding.I_(v,l) ^((k)) is the extrinsic mutual information (EMI) sent fromvariable nodes to check nodes (depending on channel mutual informationand the EMI sent from check nodes to variable nodes) and is computed foriteration l and edge-type k as:

$\begin{matrix}{I_{v,l}^{(k)} = {\sum\limits_{d = d_{v\; 1}}^{d_{n_{v}}}\;{\lambda_{d}^{(k)}{{J( \sqrt{{J^{- 1}( I_{r^{b}} )}^{2} + {( {d^{(k)} - 1} )( {J^{- 1}( I_{c,l}^{(k)} )} )^{2}} + {\sum\limits_{{{(t)} = 1}{t \neq {(k)}}}^{T}\;{( d^{(l)} ) \cdot {J^{- 1}( I_{c,l}^{(t)} )}^{2}}}} )}.}}}} & (3.1)\end{matrix}$

The inputs are the channels mutual information I_(r). The J functioncomputes the channel mutual information from the channel variance basedon the channel SNR. The J function and its inverse J⁻¹ are given in theliterature for AWGN channel and can be used for Rayleigh fadingchannels.

I_(c,l) ^((k)) is the extrinsic mutual information(EMI) sent from checknodes to variable nodes (depending on the EMI sent from variable nodesto check nodes) and is computed for iteration l and edge-type k as:

$\begin{matrix}{I_{c,l}^{(k)} \approx {\sum\limits_{d = d_{v\; 1}}^{d_{n_{c}}}\;{{\rho_{d}^{(k)}( {1 - {J( \sqrt{( {d^{(k)} - 1} )( {{J^{- 1}( {1 - I_{v,{l - 1}}^{(k)}} )}^{2} + {\sum\limits_{{{(t)} = 1}{t \neq {(k)}}}^{T}{( {d^{(t)} + 1} ) \cdot {J^{- 1}( {1 - I_{v,{l - 1}}^{(t)}} )}^{2}}}} )} )}} )}.}}} & (3.2)\end{matrix}$

Both equations enable the threshold computation of Multi-Edge-Typedegree distributions by tracking the EMI for given channel SNR valuesover sufficient iterations. If the EMI of the variable nodes eventuallyconverges to one, the channel SNR values are over the degreedistribution threshold values. This behavior is shown in the two“Multi-Edge-Type” extrinsic information exchange (EXIT) charts depictedin FIGS. 12 and 13 for a degree distribution with two differentedge-types. In the first one (FIG. 12), channel SNR values are above theensemble thresholds SNR^(th1) and SNR^(th2). The variable node EMIconverges to one. In the second (FIG. 13), channel SNR values are belowthreshold values and variable node EMI converges to a value smaller thanone.

To guide the following optimization process, the area between variablenode curve and check node curve is approximated as follows. Any otherapproximation procedure can also be used.

$\begin{matrix}{A^{(k)} = {{\sum\limits_{l = 1}^{l = {l_{m\;{ax}} - 1}}\;{( {{\frac{1}{2}( {I_{v,{l + 1}} - I_{v,l}} )} + I_{v,l}} )( {I_{c,{l + 1}} - I_{c,l}} )}} - {\sum\limits_{l = 2}^{l = l_{m\;{ax}}}{( {{\frac{1}{2}( {I_{v,{l + 1}} - I_{v,l}} )} + I_{v,l}} )( {I_{c,{l + 2}} - I_{c,{l + 1}}} )}}}} & (3.3)\end{matrix}$

For the optimization of the Multi-Edge-Type degree distributions amodified mixed-integer-discrete-continuous differential evolutionalgorithm may be used.

The main phases of this algorithm are: 1. Initialization: A number of NPensembles (i.e. L+R) are either randomly generated or chosen fromprevious optimization runs to form the first generation G. The channelSNRs (SNR₁, SNR₂, SNR₃, etc.) and other channel characteristics(Rayleigh factors etc.) are set to values above the combined channelcapacity. The best ensemble is determined according the cost functionspecified in step 3 and labeled x_(best).

Crossover: A number of NP new candidates v for generation G+1 arecreated from the current generation G ensembles.

The crossover operation creating new variables for candidates may bebased with probability of or_(j)>O_(R) on x_(best) and the variabledifferences between random ensembles x_(b) and x_(c). or_(j) is a randomvariable drawn for each candidate v while O_(R) is fixed throughout thecode design process or varied with lower frequency than or_(j).Furthermore, the ensembles are based on a random ensemble x_(a) and thedifferences of random ensembles x_(b) and x_(c) with probability oror_(j)≤O_(R).

A variable (i.e. node fraction or node degree) of candidate v_(j) iseither directly copied from generation G's ensemble x_(j) or based onthe crossover operation using variable differences with probabilitycr_(j,j)>C_(R).

cr_(i,j) is a random variable drawn for each variable of the candidate vwhile C_(R) is fixed throughout the code design process or varied withlower frequency than cr_(i,j).

The procedure for the i-th variable of the j-th ensemble in G generatingvariable V_(i,j) as candidate for G+1 is:

$v_{i,j}^{({G + 1})} = \{ \begin{matrix}{x_{i,{best}}^{(G)} + {F( {x_{i,b}^{(G)} - x_{i,c}^{(G)}} )}} & {{{if}\mspace{14mu}{cr}_{i,j}} < {C_{R}\mspace{14mu}{and}\mspace{14mu}{or}_{j}} > O_{R}} \\{x_{i,a}^{(G)} + {F( {x_{i,b}^{(G)} - x_{i,c}^{(G)}} )}} & {{{if}\mspace{14mu}{cr}_{i,j}} < {C_{R}\mspace{14mu}{and}\mspace{14mu}{or}_{j}} \leq O_{R}} \\x_{i,j}^{(G)} & {else}\end{matrix} $

Selection: The candidates are selected as members G+1 according to, ifthey can be decoded at the channel SNRs, their VN/CN curve area and howwell they satisfy the socket constraints, receive constraints and rateconstraint associated with multi-edge type ensembles. The cost computedaccording to the disclosed Multi-Edge-Type EXIT chart analysis may bewritten as:

$f_{thres} = \{ \begin{matrix}{\Sigma_{k = 1}^{T}A^{(k)}} & {{{{if}\mspace{14mu}{SNR}_{s}^{th}} < {SNR}_{s}},{{\forall s} = ( {1,\ldots\mspace{14mu},D} )}} \\c_{t} & {{else}.}\end{matrix} $

If an ensemble can be decoded at current SNRs, its VN/CN curve areasummed over all edge types is used. If it cannot be decoded at currentSNRs, it is associated with cost c_(t) which is usually set very high todiscourage further using this ensemble. For threshold analysis, theensembles are transformed from the node perspective L and R to the edgeperspective λ and σ.

The threshold cost f_(thres) combines the absolute deviation values forsocket constraints ϵ_(soc) over all edge-types, absolute deviationvalues for receive constraints over all different channels ϵ_(rec) andthe absolute deviation from the rate constraint ϵ_(rat) to derive thetotal ensemble cost as:

${f_{cost}(x)} = {( {{f_{thres}(x)} + c_{p}} ) \cdot {\prod\limits_{k = 1}^{T}\;{( {\epsilon_{soc}^{(k)}r_{soc}} )^{p_{soc}} \cdot {\prod\limits_{s = 1}^{D}{( {\epsilon_{rec}^{s}r_{rec}} )^{p_{rec}} \cdot ( {\epsilon_{rat}r_{rat}} )^{p_{rat}}}}}}}$

The parameters r and p are used to increase the penalty for constraintdeviations linearly and exponentially. Increases in the number ofedge-types and increases in the number of channels usually makeincreases in r_(soc)/p_(soc) and r_(rec)/p_(rec) necessary. C_(p) is aconstant factor ensuring that constraint deviations are not weightedarbitrary strong as f_(thres)(x) decreases. The j-th candidate isselected for G+1 if f_(cost)(v_(j))≤f_(cost)(x). Else x_(j) from G iskept for G+1.

Repetition: Phases 2, 3, 4 may be repeated till f_(cost)(x_(best))decreased below a certain value (i.e. is good enough). For the case thatf_(cost)(x_(best)) is good enough, the channel SNRs may be decreased andthe repetitions are restarted at the crossover phase. If crossover andselection do yield good enough ensembles after an evolutionary iterationlimit, the optimization process may be stopped and LDPC codes can beconstructed from the degree distributions L and R.

In the following, Construction of a specific Multi-Edge LDPC Code basedon the Degree Distributions from the Optimization procedure isdescribed.

The parity check matrix H for a multi-edge LDPC code with block length nmay be generated by the novel multi-edge type edge-interleaver. First,each of the n columns (i.e. variable nodes of the Tanner graph) of H areset to zero and associated with their respective channel inputs. Theneach column is filled according to the variable node degree distributionL at random positions with is for edge-type-1 with 2s for edge-type-2etc. For the example of L=0.5r₁x₁ ⁷x₂ ³+0.5r₂x₂ ¹⁰, the first n/2columns are associated with channel-1, each of these columns has 7 is atrandom positions and 3 2 s at random mutually different positions. Theremaining n/2 columns are associated with channel-2, each of thesecolumns has 10 2s at random positions. Afterward, the rows of H arenormalized to the check node degree distribution R. For this, thedeviation from R is computed for each row (i.e. how many is, 2s etc. areactually in each row minus the number of edge-type-1, edge-type-1 etc.specified by R).

Beginning at the first row: If a row has x too many edges of anedge-type-y, x column positions with this edge-type are randomly chosenfrom within this row. Along these columns, from the current rowsposition downwards, the edge-type-y is exchanged with a 0 from a randomposition.

If a row has x too few edges of an edge-type-y, x column positions witha 0 are randomly chosen from within this row. Along these columns, fromthe current rows position downwards, the 0 is exchanged with aedge-type-y from a random position. This guarantees that the optimalstructure defined by L and R is in the parity check matrix H. Finally,all edges (i.e. 1s, 2s, etc.) are rounded to 1.

The novel method as described above may also be referred to as“Multi-Edge Type EXIT Analysis” for deriving a Multi-Edge Type degreedistribution that is optimized for a given set of SINR values. Adifferential evolutionary optimization algorithm may be integrated intothe upper method for Multi-Edge LDPC threshold optimization. TheMulti-Edge Type degree distribution may be transformed to a specificMulti-Edge Type LDPC code through random interleaving. This solutionmaintains the structure of the degree distribution derived from theoptimization stage. The transforming the Multi-Edge Type degreedistribution to a specific Multi-Edge Type LDPC code may be performedthrough random interleaving. This solution maintains the structure ofthe degree distribution derived from the optimization stage.

FIG. 12 is a graph illustrating an exemplary extrinsic informationexchange (EXIT) chart 1200 for a degree distribution of a first edgetype. The first curve 1201 depicts extrinsic mutual information (EMI)from check nodes (CN) versus extrinsic mutual information from variablenodes (VN) for edge-type 1. The second curve 1202 depicts extrinsicmutual information (EMI) from check nodes (CN) versus extrinsic mutualinformation from variable nodes (VN) for edge-type 2.

In the EXIT chart of FIG. 12, channel SNR values are above the ensemblethresholds SNR^(th1) and SNR^(th2). The variable node EMI converges toone.

FIG. 13 is a graph illustrating an exemplary extrinsic informationexchange (EXIT) chart 1300 for a degree distribution of a second edgetype. The first curve 1301 depicts extrinsic mutual information (EMI)from check nodes (CN) versus extrinsic mutual information from variablenodes (VN) for edge-type 1. The second curve 1302 depicts extrinsicmutual information (EMI) from check nodes (CN) versus extrinsic mutualinformation from variable nodes (VN) for edge-type 2. These curves showthe extrinsic information for a given edge type (as mentioned above). Inone approach (that may be sub-optimal), the difference between thecurves of a given edge type may be optimized. In a second approach (thatmay be the optimal case) a “surface” (or other shape) may be usedrepresenting the extrinsic information of all given edge types and thedifference between those shapes (which can be a volume or similar) isminimized. Both approaches can be applied with their inherentproperties.

In the EXIT chart of FIG. 13, channel SNR values are below thresholdvalues and variable node EMI converges to a value smaller than one.

The methods, systems and devices described herein may be implemented assoftware in a Digital Signal Processor (DSP), in a micro-controller orin any other side-processor or as hardware circuit on a chip or withinan application specific integrated circuit (ASIC).

Embodiments described in this disclosure can be implemented in digitalelectronic circuitry, or in computer hardware, firmware, software, or incombinations thereof, e.g. in available hardware of mobile devices or innew hardware dedicated for processing the methods described herein.

The present disclosure also supports a computer program productincluding computer executable code or computer executable instructionsthat, when executed, causes at least one computer to execute theperforming and computing blocks described herein, in particular themethods 1000 and 1100 as described above with respect to FIGS. 10 and11. Such a computer program product may include a readable storagemedium storing program code thereon for use by a processor, the programcode comprising instructions for performing any of the method 1000, 1100as described above.

EXAMPLES

The following examples pertain to further embodiments. Example 1 is amobile communication system, comprising: a first transmission pathconfigured to transmit a message according to a first radio accesstechnology; a second transmission path configured to transmit themessage according to a second radio access technology; an encoderconfigured to encode the message by a code before transmission of themessage over the first transmission path and the second transmissionpath, wherein the code comprises at least two subcodes, and wherein theencoder is configured to encode the message intended for transmissionover the first transmission path with a first subcode of the at leasttwo subcodes and to encode the message intended for transmission overthe second transmission path with a second subcode of the at least twosubcodes.

In Example 2, the subject matter of Example 1 can optionally includethat a channel code of the first radio access technology is a multi-EdgeLow Density Parity Check (LDPC) code from the beginning; or that thechannel code of the first radio access technology is a multi-Edge LDPCcode as a replacement code for an original channel code different from amulti-Edge LDPC code.

In Example 3, the subject matter of any one of Examples 1-2 canoptionally include that the first radio access technology is atechnology based on LDPC codes, in particular one of IEEE 802.11ac orIEEE 802.11n; and that the second radio access technology is amillimeter wave radio access technology.

In Example 4, the subject matter of any one of Examples 1-3 canoptionally include that the code comprising the at least two subcodescomprises a multi-edge low density parity check (LDPC) code and thateach subcode corresponds to a different edge-type having a specific nodedegree distribution.

In Example 5, the subject matter of Example 4 can optionally includethat the first subcode is based on a signal to interference and noisecharacteristic of the first transmission path; that the second subcodeis based on a signal to interference and noise characteristic of thesecond transmission path; and that the first subcode and the secondsubcode are optimized with respect to an observed multi-path propagationprofile.

In Example 6, the subject matter of Example 4 can optionally includethat the message comprises a plurality of data containers; and that thefirst subcode is allocated to a first data container of the plurality ofdata containers and the second subcode is allocated to a second datacontainer of the plurality of data containers.

In Example 7, the subject matter of Example 6 can optionally includethat the allocation of the first subcode to the first data container isbased on a signal-to-interference and noise ratio (SINR) of the firstdata container.

In Example 8, the subject matter of Example 7 can optionally includethat the first subcode is designed for allocation to a data container ofthe plurality of data containers in which an SINR is above a first SINRlevel; and that the second subcode is designed for allocation to a datacontainer of the plurality of data containers in which an SINR is belowa second SINR level.

Example 9 is a mobile receiver, comprising: a receiving circuit,configured to receive a signal comprising transmissions from a servingradio cell and a plurality of interfering radio cells, wherein thesignal is encoded by a code comprising a plurality of subcodes; and adetection circuit configured to detect a first interference scenariobased on decoding the signal with a first subcode of the plurality ofsubcodes and to detect a second interference scenario based on decodingthe signal with a second subcode of the plurality of subcodes.

In Example 10, the subject matter of Example 9 can optionally includethat the receiving circuit is configured to receive the signalcomprising a plurality of resource blocks, wherein a particular subcodeof the plurality of subcodes is allocated to a respective resource blockof the plurality of resource blocks.

In Example 11, the subject matter of Example 10 can optionally includethat the particular subcode allocated to the respective resource blockdepends on a channel gain of the respective resource block.

In Example 12, the subject matter of any one of Examples 9-11 canoptionally include that the first subcode is adapted for the firstinterference scenario and the second subcode is adapted for the secondinterference scenario.

In Example 13, the subject matter of any one of Examples 9-11 canoptionally include that the code comprises a multi-edge low densityparity check (LDPC) code comprising a plurality of multi-edge LDPCsubcodes.

Example 14 is a method for generating a super low density parity check(LDPC) code based on an LDPC code comprising a first set of variablenodes and a second set of check nodes, the method comprising: generatinga plurality of edge types for the first set of variable nodes and thesecond set of check nodes, each edge type depending on a particularradio access technology of a set of radio access technologies and achannel profile of the particular radio access technology; connecting aparticular edge type of the variable nodes with a corresponding edgetype of the check nodes according to a particular subcode of the LDPCcode; and generating the super low density parity check (LDPC) codebased on the edge type connections of the plurality of edge types.

In Example 15, the subject matter of Example 14 can optionally includethat the particular subcode of the LDPC code is optimized for theparticular radio access technology and the channel profile of theparticular radio access technology.

In Example 16, the subject matter of any one of Examples 14-15 canoptionally include: generating the channel profile of the particularradio access technology as a step function representing a statisticaldistribution of channel gains of the particular radio access technologyin a quantized representation.

In Example 17, the subject matter of any one of Examples 14-16 canoptionally include: identifying the set of radio access technologiesbased on radio access technologies that can be combined in a transmitteror receiver pair and based on a required link characteristic of thetransmitter or receiver pair.

In Example 18, the subject matter of any one of Examples 14-17 canoptionally include that the LDPC code comprises a multi-edge LDPC codecomprising a plurality of multi-edge LDPC subcodes.

In Example 19, the subject matter of any one of Examples 14-18 canoptionally include: generating the super low density parity check (LDPC)code as a matrix comprising all edge type connections of the pluralityof edge types.

Example 20 is a method for generating a multi-edge LDPC code forencoding a message in a mobile communication system, the methodcomprising: generating a first set of variable nodes and a second set ofcheck nodes; generating a plurality of edge types for the first set ofvariable nodes and the second set of check nodes; and connecting an edgetype of the set of variable nodes with a corresponding edge type of thecheck nodes according to a predetermined metric that is based onextrinsic mutual information exchanged between the first set of variablenodes and the second set of check nodes on each edge type.

In Example 21, the subject matter of Example 20 can optionally include:deriving the extrinsic mutual information based on edge-perspectivedegree distributions for each edge type.

In Example 22, the subject matter of Example 21 can optionally include:deriving the edge-perspective degree distributions for each edge type bypartial derivation of node perspective distributions.

In Example 23, the subject matter of any one of Examples 20-22 canoptionally include: deriving the extrinsic mutual information based onextrinsic information exchange (EXIT) charts.

In Example 24, the subject matter of any one of Examples 20-23 canoptionally include that the predetermined metric is configured tominimize the extrinsic mutual information for each edge type.

In Example 25, the subject matter of any one of Examples 20-24 canoptionally include: encoding the message by the multi-edge LDPC code;and transmitting the encoded message over a first transmission pathaccording to a first radio access technology and a second transmissionpath according to a second radio access technology.

Example 26 is a computer readable medium on which computer instructionsare stored which when executed by a computer, cause the computer toperform the method of one of Examples 14 to 25.

Example 27 is a device for generating a super low density parity check(LDPC) code based on an LDPC code comprising a first set of variablenodes and a second set of check nodes, the device comprising: means forgenerating a plurality of edge types for the first set of variable nodesand the second set of check nodes, each edge type depending on aparticular radio access technology of a set of radio access technologiesand a channel profile of the particular radio access technology; meansfor connecting a particular edge type of the variable nodes with acorresponding edge type of the check nodes according to a particularsubcode of the LDPC code; and means for generating the super low densityparity check (LDPC) code based on the edge type connections of theplurality of edge types.

In Example 28, the subject matter of Example 27 can optionally includemeans that is configured to optimize the particular subcode of the LDPCcode for the particular radio access technology and the channel profileof the particular radio access technology.

In Example 29, the subject matter of any one of Examples 27-28 canoptionally include: means for generating the channel profile of theparticular radio access technology as a step function representing astatistical distribution of channel gains of the particular radio accesstechnology in a quantized representation.

In Example 30, the subject matter of any one of Examples 27-29 canoptionally include: means for identifying the set of radio accesstechnologies based on radio access technologies that can be combined ina transmitter/receiver pair and based on a required link characteristicof the transmitter/receiver pair.

In Example 31, the subject matter of any one of Examples 27-30 canoptionally include that the LDPC code comprises a multi-edge LDPC codecomprising a plurality of multi-edge LDPC subcodes.

In Example 32, the subject matter of any one of Examples 27-31 canoptionally include: means for generating the super low density paritycheck (LDPC) code as a matrix comprising all edge type connections ofthe plurality of edge types.

Example 33 is a device for generating a multi-edge LDPC code forencoding a message in a mobile communication system, the devicecomprising: means for generating a first set of variable nodes and asecond set of check nodes; means for generating a plurality of edgetypes for the first set of variable nodes and the second set of checknodes; and means for connecting an edge type of the variable nodes witha corresponding edge type of the check nodes according to apredetermined metric which is based on extrinsic mutual informationexchanged between the first set of variable nodes and the second set ofcheck nodes on each edge type.

In Example 34, the subject matter of Example 33 can optionally include:means for deriving the extrinsic mutual information based onedge-perspective degree distributions for each edge type.

In Example 35, the subject matter of Example 34 can optionally include:means for deriving the edge-perspective degree distributions for eachedge type by partial derivation of node perspective distributions.

In Example 36, the subject matter of any one of Examples 33-35 canoptionally include: means for deriving the extrinsic mutual informationbased on extrinsic information exchange (EXIT) charts.

In Example 37, the subject matter of any one of Examples 33-36 canoptionally include that the predetermined metric is configured tominimize the extrinsic mutual information for each edge type.

In Example 38, the subject matter of any one of Examples 33-37 canoptionally include: means for encoding the message by the multi-edgeLDPC code; and means for transmitting the encoded message over a firsttransmission path according to a first radio access technology and asecond transmission path according to a second radio access technology.

Example 39 is a system, comprising: a receiving device, configured toreceive a signal comprising transmissions from a serving radio cell anda plurality of interfering radio cells, wherein the signal is encoded bya code comprising a plurality of subcodes; and a detection deviceconfigured to detect a first interference scenario based on decoding thesignal with a first subcode of the plurality of subcodes and to detect asecond interference scenario based on decoding the signal with a secondsubcode of the plurality of subcodes.

In Example 40, the subject matter of Example 39 can optionally includethat the receiving device is configured to receive the signal comprisinga plurality of resource blocks, wherein a particular subcode of theplurality of subcodes is allocated to a respective resource block of theplurality of resource blocks.

In Example 41, the subject matter of Example 40 can optionally includethat the particular subcode allocated to the respective resource blockdepends on a channel gain of the respective resource block.

In Example 42, the subject matter of any one of Examples 39-41 canoptionally include that the first subcode is adapted for the firstinterference scenario and the second subcode is adapted for the secondinterference scenario.

In Example 43, the subject matter of any one of Examples 39-41 canoptionally include that the code comprises a multi-edge low densityparity check (LDPC) code comprising a plurality of multi-edge LDPCsubcodes.

In Example 44, the subject matter of any one of Examples 39-43 canoptionally include that the system is implemented as an on-chip system.

In addition, while a particular feature or aspect of the disclosure mayhave been disclosed with respect to only one of several implementations,such feature or aspect may be combined with one or more other featuresor aspects of the other implementations as may be desired andadvantageous for any given or particular application. Furthermore, tothe extent that the terms “include”, “have”, “with”, or other variantsthereof are used in either the detailed description or the claims, suchterms are intended to be inclusive in a manner similar to the term“comprise”. Furthermore, it is understood that aspects of the disclosuremay be implemented in discrete circuits, partially integrated circuitsor fully integrated circuits or programming means. Also, the terms“exemplary”, “for example” and “e.g.” are merely meant as an example,rather than the best or optimal.

Although specific aspects have been illustrated and described herein, itwill be appreciated by those of ordinary skill in the art that a varietyof alternate and/or equivalent implementations may be substituted forthe specific aspects shown and described without departing from thescope of the present disclosure. This application is intended to coverany adaptations or variations of the specific aspects discussed herein.

Although the elements in the following claims are recited in aparticular sequence with corresponding labeling, unless the claimrecitations otherwise imply a particular sequence for implementing someor all of those elements, those elements are not necessarily intended tobe limited to being implemented in that particular sequence.

The invention claimed is:
 1. A mobile communication system, comprising:a first transmission path configured to transmit a message according toa first radio access technology; a second transmission path configuredto transmit the message according to a second radio access technology;and an encoder configured to encode the message via a code block beforetransmission of the message over the first transmission path and thesecond transmission path, wherein the code block comprises at least afirst code block portion and a second code block portion, and whereinthe encoder is configured to encode the message intended fortransmission over the first transmission path with the first code blockportion, and to encode the message intended for transmission over thesecond transmission path with the second code block portion.
 2. Themobile communication system of claim 1, wherein a channel code of thefirst radio access technology is a Low Density Parity Check (LDPC) code.3. The mobile communication system of claim 1, wherein at least one ofthe first radio access technology or the second radio access technologyis a millimeter wave radio access technology.
 4. The mobilecommunication system of claim 1, wherein the message comprises aplurality of data segments, wherein the first code block portion isallocated to a first data segment of the plurality of data segments, andwherein the second code block is allocated to a second data segment ofthe plurality of data segments.
 5. A receiver, comprising: demodulatorcircuitry configured to receive a message transmitted via a firsttransmission path according to a first radio access technology and toreceive the message transmitted via a second transmission path accordingto a second radio access technology, wherein the message is encoded by acode block comprising at least a first code block portion and a secondcode block portion; and decoder circuitry configured to decode themessage by allocating the first code block portion to a first segment ofthe message and allocating the second code block portion to a secondsegment of the message.
 6. The receiver of claim 5, wherein the codeblock comprises a low density parity check (LDPC) code.
 7. The receiverof claim 5, wherein at least one of the first radio access technology orthe second radio access technology is a millimeter wave radio accesstechnology.
 8. The receiver of claim 5, wherein at least one of thefirst radio access technology or the second radio access technology is along-term evolution (LTE) radio access technology.
 9. The receiver ofclaim 5, wherein the message is encoded by the code block comprising atleast the first code block portion and the second code block portionbefore transmission of the message over the first transmission path andthe second transmission path, the message for transmission over thefirst transmission path being encoded with the first code block portion,and the message for transmission over the second transmission path beingencoded with the second code block portion.
 10. A transmitter,comprising: encoder circuitry configured to encode a message via a codeblock prior to transmission of the message over a first transmissionpath and a second transmission path; first modulator circuitryconfigured to transmit the message via the first transmission pathaccording to a first radio access technology; and second modulatorcircuitry configured to transmit the message via the second transmissionpath according to a second radio access technology, wherein the codeblock comprises at least a first code block portion and a second codeblock portion, and wherein the encoder circuitry is configured to encodethe message intended for transmission over the first transmission pathwith the first code block portion, and to encode the message intendedfor transmission over the second transmission path with the second codeblock portion.
 11. The transmitter of claim 10, wherein at least one ofthe first radio access technology or the second radio access technologyis a millimeter wave radio access technology.
 12. The transmitter ofclaim 10, wherein the message comprises a plurality of data segments,wherein the first code block portion is allocated to a first datasegment of the plurality of data segments, and wherein the second codeblock is allocated to a second data segment of the plurality of datasegments.
 13. The transmitter of claim 10, wherein at least one of thefirst radio access technology or the second radio access technology is along-term evolution (LTE) radio access technology.